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Understanding
9/11Validation on selected breast cancer drugs of physicochemical features by using machine learning models
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- Publication date
- 2024-06-01
- Usage
- Attribution-ShareAlike 4.0 International
- Topics
- Cluster validation, Clustering, Combinational therapy, Fuzzy clustering, Hopkins statistic, Physico chemical properties
- Collection
- journals_contributions; journals
- Language
- English
- Item Size
- 7.4M
Breast cancer is one of the leading causes of death among females
today. The elbow approach determines the ideal number of clusters after
determining that the Dataset is highly cluster able with the Hopkins statistic.
Three distinct groups with distinct differences were produced using the dataset's
proposed expectation maximization fuzzy k-means clustering algorithm (PEMFKM).
Different fuzzy clustering techniques, such as fuzzy k-means (FKM), fuzzy k-means
with entropy (FKM.ENT), fuzzy k-means with entropy and noise (FKM.ENT.NOISE),
Gustafson and Kessel - like fuzzy k-means (FKM.GK), Gustafson and Kessel - like
fuzzy k-means with entropy regularization (FKM.GK.ENT), Gustafson and Kessel -
like fuzzy k-means with entropy regularization and noise (FKM.GK.ENT.NOISE),
and PEMFKM, are evaluated. The partition
coefficient (PC), partition
entropy (PE), and Modified partition
coefficient index (MPC) index values are better for FKM.GK than the suggested
PEMFKM method. When compared to the FKM.GK method, the index values for the
proposed PEMFKM algorithm have superior results for the parameters Silhouette
(SIL), Xie and Beni index (XB), and fuzzy silhouette index (SIL.F). The results
shows that the PEMFKM algorithm will provide better clusters and that the drugs
in a given cluster may be combined for use in combination therapy for breast
cancer treatment.
today. The elbow approach determines the ideal number of clusters after
determining that the Dataset is highly cluster able with the Hopkins statistic.
Three distinct groups with distinct differences were produced using the dataset's
proposed expectation maximization fuzzy k-means clustering algorithm (PEMFKM).
Different fuzzy clustering techniques, such as fuzzy k-means (FKM), fuzzy k-means
with entropy (FKM.ENT), fuzzy k-means with entropy and noise (FKM.ENT.NOISE),
Gustafson and Kessel - like fuzzy k-means (FKM.GK), Gustafson and Kessel - like
fuzzy k-means with entropy regularization (FKM.GK.ENT), Gustafson and Kessel -
like fuzzy k-means with entropy regularization and noise (FKM.GK.ENT.NOISE),
and PEMFKM, are evaluated. The partition
coefficient (PC), partition
entropy (PE), and Modified partition
coefficient index (MPC) index values are better for FKM.GK than the suggested
PEMFKM method. When compared to the FKM.GK method, the index values for the
proposed PEMFKM algorithm have superior results for the parameters Silhouette
(SIL), Xie and Beni index (XB), and fuzzy silhouette index (SIL.F). The results
shows that the PEMFKM algorithm will provide better clusters and that the drugs
in a given cluster may be combined for use in combination therapy for breast
cancer treatment.
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